Trajectories of pain and opioid use up to one year after surgery: analysis of a European registry

BACKGROUND

Long-term opioid use after surgery is a crucial healthcare problem in North America. Data from European hospitals are scarce and differentiation of chronic pain has rarely been considered.

METHODS

In a mixed surgical cohort of the PAIN OUT registry, opioid use and chronic pain were evaluated before surgery, and 6 and 12 months after surgery (M6/M12). Subgroups with or without opioid medication and pre-existing chronic pain were analysed. M12-chronic pain was categorised as chronic postsurgical pain (CPSP) meeting the ICD-11 definition, chronic pain related to surgery not meeting the ICD-11 definition, and chronic pain unrelated to surgery. Primary endpoint was the rate of M12 opioid users. Variables associated with M12 opioid use and patient-reported outcomes were evaluated.

RESULTS

Of 2326 patients, 5.5% were preoperative opioid users; 4.4% and 3.5% took opioids at M6 and M12 (P<0.001). Chronic pain before operation and at M6/M12 was reported by 41.2%, 41.8%, and 34.7% of patients, respectively (P<0.001). The rate of M12 opioid users was highest in group unrelated (22.3%; related 8.3%, CPSP 1.5%; P<0.001). New opioid users were 1.1% (unrelated 7.1%, related 2.3%, CPSP 0.7%; P<0.001). M12 opioid users reported more pain, pain-related physical and affective interference, and needed more opioids than non-users. The predominant variable associated with M12 opioids was preoperative opioid use (estimated odds ratio [95% confidence interval]: 28.3 [14.1-56.7], P<0.001).

CONCLUSIONS

Opioid use was low in patients with CPSP, and more problematic in patients with chronic pain unrelated to surgery. A detailed assessment of chronic pain unrelated or related to surgery or CPSP is necessary.

CLINICAL TRIAL REGISTRATION

NCT02083835.

Development and internal validation of a prediction model for long-term opioid use-an analysis of insurance claims data

ABSTRACT

In the United States, a public-health crisis of opioid overuse has been observed, and in Europe, prescriptions of opioids are strongly increasing over time. The objective was to develop and validate a multivariable prognostic model to be used at the beginning of an opioid prescription episode, aiming to identify individual patients at high risk for long-term opioid use based on routinely collected data. Predictors including demographics, comorbid diseases, comedication, morphine dose at episode initiation, and prescription practice were collected. The primary outcome was long-term opioid use, defined as opioid use of either >90 days duration and ≥10 claims or >120 days, independent of the number of claims. Traditional generalized linear statistical regression models and machine learning approaches were applied. The area under the curve, calibration plots, and the scaled Brier score assessed model performance. More than four hundred thousand opioid episodes were included. The final risk prediction model had an area under the curve of 0.927 (95% confidence interval 0.924-0.931) in the validation set, and this model had a scaled Brier score of 48.5%. Using a threshold of 10% predicted probability to identify patients at high risk, the overall accuracy of this risk prediction model was 81.6% (95% confidence interval 81.2% to 82.0%). Our study demonstrated that long-term opioid use can be predicted at the initiation of an opioid prescription episode, with satisfactory accuracy using data routinely collected at a large health insurance company. Traditional statistical methods resulted in higher discriminative ability and similarly good calibration as compared with machine learning approaches.

Predictors of persistent opioid use in non-cancer older adults: a retrospective cohort study

BACKGROUND

Long-term opioid use and associated adverse outcomes have increased dramatically in recent years. Limited research is available on long-term opioid use in older adults.

OBJECTIVE

We aimed to determine the incidence and predictors of long-term or persistent opioid use (POU) amongst opioid-naïve older adults without a cancer diagnosis.

METHODS

This was a retrospective cohort study using five national administrative healthcare databases in New Zealand. We included all opioid-naïve older adults (≥65 years) who were initiated on opioid therapy between January 2013 and June 2018. The outcome of interest was POU, defined as having continuously filled ≥1 opioid prescription within 91-180 days after the index opioid prescription. Multivariable logistic regression was used to examine the predictors of POU.

RESULTS

The final sample included 268,857 opioid-naïve older adults; of these, 5,849(2.2%) developed POU. Several predictors of POU were identified. The use of fentanyl (adjusted odds ratio (AOR) = 3.61; 95% confidence interval (CI) 2.63-4.95), slow-release opioids (AOR = 3.02; 95%CI 2.78-3.29), strong opioids (AOR = 2.03; 95%CI 1.55-2.65), Charlson Comorbidity Score ≥ 3 (AOR = 2.09; 95% CI 1.78-2.46), history of substance abuse (AOR = 1.52; 95%CI 1.35-1.72), living in most socioeconomically deprived areas (AOR = 1.40; 95%CI 1.27-1.54), and anti-epileptics (AOR = 2.07; 95%CI 1.89-2.26), non-opioid analgesics (AOR = 2.05; 95%CI 1.89-2.21), antipsychotics (AOR = 1.96; 95%CI 1.78-2.17) or antidepressants (AOR = 1.50; 95%CI 1.41-1.59) medication use were the strongest predictors of POU.

CONCLUSION

A significant proportion of patients developed POU, and several factors were associated with POU. The findings will enable healthcare providers and policymakers to target early interventions to prevent POU and related adverse events.

Prevalence and predictors of long-term opioid use following orthopaedic surgery in an Australian setting: A multicentre, prospective cohort study

Opioid analgesics prescribed for the management of acute pain following orthopaedic surgery may lead to unintended long-term opioid use and associated patient harms. This study aimed to examine the prevalence of opioid use at 90 days after elective orthopaedic surgery across major city, regional and rural locations in New South Wales, Australia. We conducted a prospective, observational cohort study of patients undergoing elective orthopaedic surgery at five hospitals from major city, regional, rural, public and private settings between April 2017 and February 2020. Data were collected by patient questionnaire at the pre-admission clinic 2-6 weeks before surgery and by telephone call after 90 days following surgery. Of the 361 participants recruited, 54% (195/361) were women and the mean age was 67.7 years (standard deviation 10.1 years). Opioid use at 90 or more days after orthopaedic surgery was reported by 15.8% (57/361; 95% confidence interval (CI) 12.2-20%) of all participants and ranged from 3.5% (2/57) at a major city location to 37.8% (14/37) at an inner regional location. Predictors of long-term postoperative opioid use in the multivariable analysis were surgery performed at an inner regional location (adjusted odds ratio 12.26; 95% CI 2.2-68.24) and outer regional location (adjusted odds ratio 5.46; 95% CI 1.09-27.50) after adjusting for known covariates. Long-term opioid use was reported in over 15% of patients following orthopaedic surgery and appears to be more prevalent in regional locations in Australia.

Perioperative Use of Gabapentinoids and Risk for Postoperative Long-Term Opioid Use in Older Adults Undergoing Total Knee or Hip Arthroplasty

BACKGROUND

Gabapentinoids are recommended by guidelines as a component of multimodal analgesia to manage postoperative pain and reduce opioid use. It remains unknown whether perioperative use of gabapentinoids is associated with a reduced or increased risk of postoperative long-term opioid use (LTOU) after total knee or hip arthroplasty (TKA/THA).

METHODS

Using Medicare claims data from 2011 to 2018, we identified fee-for-service beneficiaries aged ≥ 65 years who were hospitalized for a primary TKA/THA and had no LTOU before the surgery. Perioperative use of gabapentinoids was measured from 7 days preadmission through 7 days postdischarge. Patients were required to receive opioids during the perioperative period and were followed from day 7 postdischarge for 180 days to assess postoperative LTOU (ie, ≥90 consecutive days). A modified Poisson regression was used to estimate the relative risk (RR) of postoperative LTOU in patients with versus without perioperative use of gabapentinoids, adjusting for confounders through propensity score weighting.

RESULTS

Of 52,788 eligible Medicare older beneficiaries (mean standard deviation [SD] age 72.7 [5.3]; 62.5% females; 89.7% White), 3,967 (7.5%) received gabapentinoids during the perioperative period. Postoperative LTOU was 3.8% in patients with and 4.0% in those without perioperative gabapentinoids. After adjusting for confounders, the risk of postoperative LTOU was similar comparing patients with versus without perioperative gabapentinoids (RR = 1.07; 95% confidence interval [CI] = 0.91-1.26, P = .408). Sensitivity and bias analyses yielded consistent results.

CONCLUSION

Among older Medicare beneficiaries undergoing a primary TKA/THA, perioperative use of gabapentinoids was not associated with a reduced or increased risk for postoperative LTOU.

A Predictive Model for Developing Long Term Opioid Use After Neurosurgery and Orthopedic Surgery

This study aimed to identify patient characteristics that predict long-term opioid use after an orthopedic or neurosurgery procedure. Long-term opioid use was defined as opioid use for 90 or more days following the surgical procedure. A retrospective analysis was conducted of orthopedic and neurosurgery patients 18 years and older from 01/01/2011 through 12/31/2017 (n = 12,301). Characteristics included age, sex, race, length of hospital stay, body mass index, surgical procedure specialty, presence of opioid use before and after surgery, and opioid use 90 days or more after surgery. A multiple logistic regression model was used to model characteristics predictive of long-term use of opioids. In this cohort, 32.0% of patients had prescriptions for opioids 90 or more days after surgery. Statistically significant risk factors for long-term opioid use were being Caucasian, younger (18-25 years age group) or older than age 45 and being obese. People who were African American or Black, in the 25-45 years age group, underweight, and used opioids before surgery were less likely to use opioids 90 days after surgery. Nurse anesthetist awareness of predictive characteristics of long-term opioid use can lead to alternative options to prevent opioid abuse.

Describing long-term opioid use utilizing Nordic Prescription Registers - A Norwegian example

Previous studies have defined long-term opioid use in varying ways, decreasing comparability, reproducibility, and clinical applicability of the research. Based on recommendations from recent systematic reviews, we aimed to develop a methodology to estimate the prevalence of use persisting more than three months utilizing one of the Nordic prescription registers. We used the Norwegian Prescription Register (NorPD) to extract data on all opioid dispensations between 1 January 2004 and 31 October 2019. New users of opioids (washout 365 days) were defined as long-term users if they fulfilled two criteria: 1) they had ≥2 dispensations of opioids, 91-180 days apart; 2) days 0-90 included ≥90 dispensed administration units (e.g., tablets) of opioids. Overall, there were 2,543,224 new users of opioids during the study period. Of these, 354,666 (13.9%) fulfilled the criteria for long-term opioid use at least once. Compared with those who did not fulfill the criteria (short-term users), long-term users were older, more likely women, and used tramadol, oxycodone, and buprenorphine more frequently as their first opioid. In conclusion, we found that 1/7 of opioid users continued use longer than 3 months. Future outcome research should identify the clinically most important dose requirements for long-term opioid use criteria.

Using Prescription Drug Monitoring Program Data to Assess Likelihood of Incident Long-Term Opioid Use: a Statewide Cohort Study

BACKGROUND

Limiting the incidence of opioid-naïve patients who transition to long-term opioid use (i.e., continual use for > 90 days) is a key strategy for reducing opioid-related harms.

OBJECTIVE

To identify variables constructed from data routinely collected by prescription drug monitoring programs that are associated with opioid-naïve patients' likelihood of transitioning to long-term use after an initial opioid prescription.

DESIGN

Statewide cohort study using prescription drug monitoring program data PARTICIPANTS: All opioid-naïve patients in California (no opioid prescriptions within the prior 2 years) age ≥ 12 years prescribed an initial oral opioid analgesic from 2010 to 2017.

METHODS AND MAIN MEASURES

Multiple logistic regression models using variables constructed from prescription drug monitoring program data through the day of each patient's initial opioid prescription, and, alternatively, data available up to 30 and 60 days after the initial prescription were constructed to identify probability of transition to long-term use. Model fit was determined by the area under the receiver operating characteristic curve (C-statistic).

KEY RESULTS

Among 30,569,125 episodes of patients receiving new opioid prescriptions, 1,809,750 (5.9%) resulted in long-term use. Variables with the highest adjusted odds ratios included concurrent benzodiazepine use, ≥ 2 unique prescribers, and receipt of non-pill, non-liquid formulations. C-statistics for the day 0, day 30, and day 60 models were 0.81, 0.88, and 0.94, respectively. Models assessing opioid dose using the number of pills prescribed had greater discriminative capacity than those using milligram morphine equivalents.

CONCLUSIONS

Data routinely collected by prescription drug monitoring programs can be used to identify patients who are likely to develop long-term use. Guidelines for new opioid prescriptions based on pill counts may be simpler and more clinically useful than guidelines based on days' supply or milligram morphine equivalents.

A Risk Prediction Model for Long-term Prescription Opioid Use

BACKGROUND

Tools are needed to aid clinicians in estimating their patients' risk of transitioning to long-term opioid use and to inform prescribing decisions.

OBJECTIVE

The objective of this study was to develop and validate a model that predicts previously opioid-naive patients' risk of transitioning to long-term use.

RESEARCH DESIGN

This was a statewide population-based prognostic study.

SUBJECTS

Opioid-naive (no prescriptions in previous 2 y) patients aged 12 years old and above who received a pill-form opioid analgesic in 2016-2018 and whose prescriptions were registered in the California Prescription Drug Monitoring Program (PDMP).

MEASURES

A multiple logistic regression approach was used to construct a prediction model with long-term (ie, >90 d) opioid use as the outcome. Models were developed using 2016-2017 data and validated using 2018 data. Discrimination (c-statistic), calibration (calibration slope, intercept, and visual inspection of calibration plots), and clinical utility (decision curve analysis) were evaluated to assess performance.

RESULTS

Development and validation cohorts included 7,175,885 and 2,788,837 opioid-naive patients with outcome rates of 5.0% and 4.7%, respectively. The model showed high discrimination (c-statistic: 0.904 for development, 0.913 for validation), was well-calibrated after intercept adjustment (intercept, -0.006; 95% confidence interval, -0.016 to 0.004; slope, 1.049; 95% confidence interval, 1.045-1.053), and had a net benefit over a wide range of probability thresholds.

CONCLUSIONS

A model for the transition from opioid-naive status to long-term use had high discrimination and was well-calibrated. Given its high predictive performance, this model shows promise for future integration into PDMPs to aid clinicians in formulating opioid prescribing decisions at the point of care.

Opioid prescribing and risks among commercially insured women undergoing pelvic organ prolapse repair

PURPOSE

Opioid use after surgical repair for pelvic organ prolapse (POP) is intended for short-term post-operative pain. This study compared the incidence of opioid prescribing in women undergoing POP transabdominal repair with mesh and transvaginal native tissue repair.

METHODS

A retrospective cohort of women undergoing POP transabdominal repair with mesh or transvaginal native tissue repair, was derived from a 10% random sample of enrollees from 2007 to 2015 within the IQVIA PharMetrics® Plus Database. Primary outcomes were any prescription of opioids and cumulative days of opioids prescribed in the 14- 180 days following surgical intervention. Inverse probability of treatment weights controlled for observed baseline confounders. Any opioid prescription was estimated using logistic regression and generalized linear regression for cumulative days of opioids prescribed.

RESULTS

The cohort of 49 052 women who underwent POP surgical repair included 46 813 women with transvaginal native tissue repair and 2239 women with transabdominal repair with mesh. Women with a transabdominal repair with mesh had a 1.19 (95%CI: 1.09-1.31) significantly higher odds of receiving an opioid prescription than women with transvaginal native tissue repair. Post-operatively, over 29% of women received opioid prescriptions. Mean cumulative days of post-surgical opioid prescribing was 32.2 (SD = 43.1), and was not statistically different between groups. Thirteen percent of women were prescribed opioids for 90 days or more.

CONCLUSIONS

Women undergoing POP with transabdominal mesh are more likely to receive prescriptions for opioids after surgery compared to transvaginal native tissue repair. Treatment plans that address pain while mitigating the risks associated with prolonged opioid prescribing should be employed.

Postsurgical Opioid Prescriptions and Risk of Long-term Use: An Observational Cohort Study Across the United States

OBJECTIVE

The aim of this study was to evaluate differences in risk of long-term opioid therapy after surgery among an opioid-naive population using varying cutoffs based on days supplied (DS), total morphine milligram equivalents (MME) dispensed, and quantity of pills (QTY) dispensed.

BACKGROUND

In response to the US opioid crisis, opioid prescription (Rx) limits have been implemented on a state-by-state basis beginning in 2016. However, there is limited evidence informing appropriate prescribing limits, and the effect of these policies on long-term opioid therapy.

METHODS

Using the MarketScan claims databases, we identified all opioid-naive patients undergoing outpatient surgery between July 1, 2006 and June 30, 2015. We identified the initial postsurgical opioid prescribed, examining the DS, total MME, and QTY dispensed. We used Poisson to estimate adjusted risk differences and risk ratios of long-term opioid use comparing those receiving larger versus smaller volume of opioids.

RESULTS

We identified 5,148,485 opioid-naive surgical patients. Overall, 55.5% received an opioid for postoperative pain, with median days supply = 5 and median total MME = 240. The proportion of patients receiving prescriptions above 7 DS increased from 11% in 2006 to 19% in 2015. Among those receiving postoperative opioids, 8% had long-term opioid use, and risk of long-term use was 1.16 times [95% confidence interval (CI), 1.10-1.25] higher among those receiving >7 days compared with those receiving ≤7 days. Those receiving >400 total MME (15% of patients) were at 1.17 times (95% CI, 1.10-1.25) the risk of long-term use compared with those receiving ≤400 MME.

CONCLUSIONS

Between 2005 and 2015, the amounts of opioids prescribed for postoperative pain increased dramatically, and receipt of larger volume of opioids was associated with increased risk of long-term opioid therapy.

A Brief Screening Tool for Opioid Use Disorder: EMPOWER Study Expert Consensus Protocol

Growing concerns about the safety of long-term opioid therapy and its uncertain efficacy for non-cancer pain have led to relatively rapid opioid deprescribing in chronic pain patients who have been taking opioid for years. To date, empirically supported processes for safe and effective opioid tapering are lacking. Opioid tapering programs have shown high rates of dropouts and increases in patient distress and suicidal ideation. Therefore, safe strategies for opioid deprescribing that are more likely to succeed are urgently needed. In response to this demand, the EMPOWER study has been launched to examine the effectiveness of behavioral medicine strategies within the context of patient-centered opioid tapering in outpatient settings (https://empower.stanford.edu/). The EMPOWER protocol requires an efficient process for ensuring that collaborative opioid tapering would be offered to the most appropriate patients while identifying patients who should be offered alternate treatment pathways. As a first step, clinicians need a screening tool to identify patients with Opioid Use Disorder (OUD) and to assess for OUD severity. Because such a tool is not available, the study team composed of eight chronic pain and/or addiction experts has extended a validated screening instrument to develop a brief and novel consensus screening tool to identify OUD and assess for OUD severity for treatment stratification. Our screening tool has the potential to assist busy outpatient clinicians to assess OUD among patients receiving long-term opioid therapy for chronic pain.

Patterns of Opioid and Benzodiazepine Use in Opioid-Naïve Patients with Newly Diagnosed Low Back and Lower Extremity Pain

BACKGROUND

The morbidity and mortality associated with opioid and benzodiazepine co-prescription is a pressing national concern. Little is known about patterns of opioid and benzodiazepine use in patients with acute low back pain or lower extremity pain.

OBJECTIVE

To characterize patterns of opioid and benzodiazepine prescribing among opioid-naïve, newly diagnosed low back pain (LBP) or lower extremity pain (LEP) patients and to investigate the relationship between benzodiazepine prescribing and long-term opioid use.

DESIGN/SETTING

We performed a retrospective analysis of a commercial database containing claims for more than 75 million enrollees in the USA.

PARTICIPANTS

Participants were adult patients newly diagnosed with LBP or LEP between 2008 and 2015 who did not have a red flag diagnosis, had not received an opioid prescription in the 6 months prior to diagnosis, and had 12 months of continuous enrollment after diagnosis.

MAIN OUTCOMES AND MEASURES

Among patients receiving at least one opioid prescription within 12 months of diagnosis, we defined discrete patterns of benzodiazepine prescribing-continued use, new use, stopped use, and never use. We tested the association of these prescription patterns with long-term opioid use, defined as six or more fills within 12 months.

RESULTS

We identified 2,497,653 opioid-naïve patients with newly diagnosed LBP or LEP. Between 2008 and 2015, 31.9% and 11.5% of these patients received opioid and benzodiazepine prescriptions, respectively, within 12 months of diagnosis. Rates of opioid prescription decreased from 34.8% in 2008 to 27.0% in 2015 (P < 0.001); however, prescribing of benzodiazepines only decreased from 11.6% in 2008 to 10.8% in 2015. Patients with continued or new benzodiazepine use consistently used more opioids than patients who never used or stopped using benzodiazepines during the study period (one-way ANOVA, P < 0.001). For patients with continued and new benzodiazepine use, the odds ratio of long-term opioid use compared with those never prescribed a benzodiazepine was 2.99 (95% CI, 2.89-3.08) and 2.68 (95% CI, 2.62-2.75), respectively.

LIMITATIONS

This study used administrative claims analyses, which rely on accuracy and completeness of diagnostic, procedural, and prescription codes.

CONCLUSION

Overall opioid prescribing for low back pain or lower extremity pain decreased substantially during the study period, indicating a shift in management within the medical community. Rates of benzodiazepine prescribing, however, remained at approximately 11%. Concurrent prescriptions of benzodiazepines and opioids after LBP or LEP diagnosis were associated with increased risk of long-term opioid use.

Economic burden of opioid-induced constipation among long-term opioid users with noncancer pain

BACKGROUND

Opioid-induced constipation (OIC) can be a debilitating side effect of opioid therapy and may result in increased medical costs. The published data on the economic burden of OIC among long-term opioid users are limited.

OBJECTIVE

To assess the economic burden of OIC in patients with noncancer pain in a managed care population in the United States.

METHODS

This retrospective study used 2007-2011 data from the Truven Health MarketScan Commercial and Medicare databases. The study included adults with ≥12 months of insurance enrollment before and after starting long-term (≥90 days) use of opioids. Patients were excluded if they had cancer or a diagnosis of drug abuse or drug dependence during the study period, or if they had constipation or bowel obstruction within 90 days before starting opioid therapy during the study period. OIC was identified by International Classification of Diseases, Ninth Edition codes for constipation (564.0) or bowel obstruction (560.x) within 12 months of the initiation of an opioid. Patients with OIC were identified in the nonelderly, elderly (age ≥65 years), and long-term care populations. Differences in costs and healthcare resource utilization were calculated using propensity scoring.

RESULTS

A total of 13,808 nonelderly (age, 48.6 ± 10.4 years; female, 50%) and 2958 elderly patients (age, 78.7 ± 8.1 years; female, 70%) met the study inclusion criteria. Of 401 nonelderly and 194 elderly patients with OIC, 85 patients initiated opioid therapy in a long-term care facility (age, 80.7 ± 11.6 years; female, 77%). After matching by key covariates, patients with OIC had significantly more hospital admissions than patients without OIC (nonelderly, 33% vs 22%, respectively; P <.001; elderly, 51% vs 31%, respectively; P <.001) and longer inpatient stays (nonelderly, 3.0 ± 8.4 days vs 1.0 ± 3.0 days, respectively; P <.001; elderly, 5.2 ± 12.2 days vs 2.1 ± 4.0 days, respectively; P <.001). The group with OIC had significantly higher total healthcare costs than the group without OIC in all 3 study cohorts (nonelderly, $23,631 ± $67,209 vs $12,652 ± $19,717, respectively; elderly, $16,923 ± $38,191 vs $11,117 ± $19,525, respectively; long-term care, $16,000 ± $22,897 vs $14,437 ± $25,690, respectively; all P <.05).

CONCLUSION

To the best of our knowledge, this is the first study to analyze the economic impact of long-term use of opioids among patients with OIC, using real-world data. The findings underscore the significant economic burden associated with long-term opioid use for noncancer pain in a managed care population. Effective therapies for OIC may reduce the associated economic burden and improve quality of life for long-term opioid users.